Abstract: A system for microsurgery includes a first assembly and a second assembly, each including: (1) a planar remote center of motion (RCM) device configured to constrain motion of a surgical instrument attached to the planar RCM device such that an axis of the surgical instrument passes through the RCM while remaining in a planar region defined based on a rotational orientation of the planar RCM device; and (2) a rotational device attached to the planar RCM device and configured such that an axis of rotation of the rotational device passes through the remote center of motion. The rotational orientation of the planar RCM device is defined about the axis of rotation. The first assembly and the second assembly are configured to be positioned such that a distance between the remote centers of motion of the first assembly and the second assembly is no greater than two centimeters.

Abstract: Disclosed is a therapeutic composition comprising human embryonic stem cell-derived micro vesicles, and methods of their use, including treatment of eye pathologies and of obtaining retinal neural cells and retinal stem cells.

Abstract: A system for microsurgery includes a first assembly and a second assembly, each including: (1) a planar remote center of motion (RCM) device configured to constrain motion of a surgical instrument attached to the planar RCM device such that an axis of the surgical instrument passes through the RCM while remaining in a planar region defined based on a rotational orientation of the planar RCM device; and (2) a rotational device attached to the planar RCM device and configured such that an axis of rotation of the rotational device passes through the remote center of motion. The rotational orientation of the planar RCM device is defined about the axis of rotation. The first assembly and the second assembly are configured to be positioned such that a distance between the remote centers of motion of the first assembly and the second assembly is no greater than two centimeters.

Abstract: A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors.

Abstract: A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors.

Abstract: A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors.

Abstract: An equipment rack includes a shelf and a backplane which supports and receives an electronic module having a rear connector. The backplane includes a complementary connector for engaging the rear connector along an insertion axis. An extraction tool is coupled to the shelf and to the module for disengaging the rear connector from the complementary connector while producing an extraction force having a first component substantially along the insertion axis and a second component substantially perpendicular to the insertion axis. A hold-down bracket is coupled to the module and slidably engages the shelf to counteract the second component of the extraction force until the rear connector disengages from the complementary connector and thus avoids upward movement of the module.

Abstract: A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors.

Abstract: The architecture of a communication network comprising a plurality of nodes interconnected by a transmission facility having a bandwidth divisible into channels of respective bandwidths, e.g., optical fiber, is enhanced by fully sharing the network bandwidth among the network nodes, such that each pair of nodes, on periodic basis, dynamically establishes respective direct links to each of the other network nodes, in which a direct link is formed from a group of channels obtained from the network bandwidth. The remaining bandwidth is then used to form a pool of bandwidth which is shared among the network nodes on a dynamic basis, such as, for example, establishing a communication path between a pair of nodes to route a call from one node to the other node of the pair of nodes.

Abstract: A passive cooled electronic chassis is used in a computer unit, or a display unit, or an actuator unit, or a like unit. The chassis overcomes the problem of the prior art chassis which has an active cooling system with parts that were difficult to operate, maintain and repair. The passive cooled electronic chassis includes an aluminum core member which has an electrical insulator sheet, a printed wiring board which bears against the electrical insulator sheet and which has a set of electrical components that are wired to the board, a housing which has a center wall that has recesses having potting material for receiving and embedding respective electrical components. The chassis provides a first heat flow path from such component through the printed circuit board and electrical insulator sheet and core member and center wall to exterior fins. The chassis also provides a second heat flow path from such component through the potting material and the center wall to the exterior fins.